sync with upstream @ v0.6.4.post1

opendatahub-io · Nov 20, 2024 · c41adbd · c41adbd
2 parents 1024f99 + a6221a1
commit c41adbd
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diff --git a/.buildkite/lm-eval-harness/configs/Meta-Llama-3.2-1B-Instruct-INT8-compressed-tensors.yaml b/.buildkite/lm-eval-harness/configs/Meta-Llama-3.2-1B-Instruct-INT8-compressed-tensors.yaml
@@ -0,0 +1,11 @@
+# bash .buildkite/lm-eval-harness/run-lm-eval-gsm-vllm-baseline.sh -m neuralmagic/Llama-3.2-1B-Instruct-quantized.w8a8 -b "auto" -l 1000 -f 5 -t 1
+model_name: "neuralmagic/Llama-3.2-1B-Instruct-quantized.w8a8"
+tasks:
+- name: "gsm8k"
+  metrics:
+  - name: "exact_match,strict-match"
+    value: 0.356
+  - name: "exact_match,flexible-extract"
+    value: 0.358
+limit: 1000
+num_fewshot: 5
diff --git a/.buildkite/lm-eval-harness/configs/models-small.txt b/.buildkite/lm-eval-harness/configs/models-small.txt
@@ -1,6 +1,6 @@
 Meta-Llama-3-8B-Instruct.yaml
 Meta-Llama-3-8B-Instruct-FP8-compressed-tensors.yaml
-Meta-Llama-3-8B-Instruct-INT8-compressed-tensors.yaml
+Meta-Llama-3.2-1B-Instruct-INT8-compressed-tensors.yaml
 Meta-Llama-3-8B-Instruct-INT8-compressed-tensors-asym.yaml
 Meta-Llama-3-8B-Instruct-nonuniform-compressed-tensors.yaml
 Meta-Llama-3-8B-Instruct-Channelwise-compressed-tensors.yaml

diff --git a/.buildkite/lm-eval-harness/run-lm-eval-gsm-hf-baseline.sh b/.buildkite/lm-eval-harness/run-lm-eval-gsm-hf-baseline.sh
@@ -41,6 +41,6 @@ while getopts "m:b:l:f:" OPT; do
 done
 
 lm_eval --model hf \
-  --model_args pretrained=$MODEL,parallelize=True \
-  --tasks gsm8k --num_fewshot $FEWSHOT --limit $LIMIT \
-  --batch_size $BATCH_SIZE
+  --model_args "pretrained=$MODEL,parallelize=True" \
+  --tasks gsm8k --num_fewshot "$FEWSHOT" --limit "$LIMIT" \
+  --batch_size "$BATCH_SIZE"
diff --git a/.buildkite/lm-eval-harness/run-lm-eval-gsm-vllm-baseline.sh b/.buildkite/lm-eval-harness/run-lm-eval-gsm-vllm-baseline.sh
@@ -46,6 +46,6 @@ while getopts "m:b:l:f:t:" OPT; do
 done
 
 lm_eval --model vllm \
-  --model_args pretrained=$MODEL,tensor_parallel_size=$TP_SIZE,distributed_executor_backend="ray",trust_remote_code=true,max_model_len=4096 \
-  --tasks gsm8k --num_fewshot $FEWSHOT --limit $LIMIT \
-  --batch_size $BATCH_SIZE
+  --model_args "pretrained=$MODEL,tensor_parallel_size=$TP_SIZE,distributed_executor_backend=ray,trust_remote_code=true,max_model_len=4096" \
+  --tasks gsm8k --num_fewshot "$FEWSHOT" --limit "$LIMIT" \
+  --batch_size "$BATCH_SIZE"
diff --git a/.buildkite/lm-eval-harness/run-tests.sh b/.buildkite/lm-eval-harness/run-tests.sh
@@ -30,7 +30,7 @@ while getopts "c:t:" OPT; do
 done
 
 # Parse list of configs.
-IFS=$'\n' read -d '' -r -a MODEL_CONFIGS < $CONFIG
+IFS=$'\n' read -d '' -r -a MODEL_CONFIGS < "$CONFIG"
 
 for MODEL_CONFIG in "${MODEL_CONFIGS[@]}"
 do

diff --git a/.buildkite/nightly-benchmarks/scripts/convert-results-json-to-markdown.py b/.buildkite/nightly-benchmarks/scripts/convert-results-json-to-markdown.py
@@ -56,7 +56,7 @@
 
 def read_markdown(file):
     if os.path.exists(file):
-        with open(file, "r") as f:
+        with open(file) as f:
             return f.read() + "\n"
     else:
         return f"{file} not found.\n"
@@ -75,14 +75,14 @@ def results_to_json(latency, throughput, serving):
     # collect results
     for test_file in results_folder.glob("*.json"):
 
-        with open(test_file, "r") as f:
+        with open(test_file) as f:
             raw_result = json.loads(f.read())
 
         if "serving" in str(test_file):
             # this result is generated via `benchmark_serving.py`
 
             # attach the benchmarking command to raw_result
-            with open(test_file.with_suffix(".commands"), "r") as f:
+            with open(test_file.with_suffix(".commands")) as f:
                 command = json.loads(f.read())
             raw_result.update(command)
 
@@ -97,7 +97,7 @@ def results_to_json(latency, throughput, serving):
             # this result is generated via `benchmark_latency.py`
 
             # attach the benchmarking command to raw_result
-            with open(test_file.with_suffix(".commands"), "r") as f:
+            with open(test_file.with_suffix(".commands")) as f:
                 command = json.loads(f.read())
             raw_result.update(command)
 
@@ -119,7 +119,7 @@ def results_to_json(latency, throughput, serving):
             # this result is generated via `benchmark_throughput.py`
 
             # attach the benchmarking command to raw_result
-            with open(test_file.with_suffix(".commands"), "r") as f:
+            with open(test_file.with_suffix(".commands")) as f:
                 command = json.loads(f.read())
             raw_result.update(command)
 

diff --git a/.buildkite/nightly-benchmarks/scripts/generate-nightly-markdown.py b/.buildkite/nightly-benchmarks/scripts/generate-nightly-markdown.py
@@ -72,15 +72,15 @@ def main(args):
 
     # collect results
     for test_file in results_folder.glob("*_nightly_results.json"):
-        with open(test_file, "r") as f:
+        with open(test_file) as f:
             results = results + json.loads(f.read())
 
     # generate markdown table
     df = pd.DataFrame.from_dict(results)
 
     md_table = tabulate(df, headers='keys', tablefmt='pipe', showindex=False)
 
-    with open(args.description, "r") as f:
+    with open(args.description) as f:
         description = f.read()
 
     description = description.format(

diff --git a/.buildkite/nightly-benchmarks/scripts/launch-server.sh b/.buildkite/nightly-benchmarks/scripts/launch-server.sh
@@ -50,58 +50,54 @@ launch_trt_server() {
   git clone https://github.com/triton-inference-server/tensorrtllm_backend.git
   git lfs install
   cd tensorrtllm_backend
-  git checkout $trt_llm_version
-  tensorrtllm_backend_dir=$(pwd)
+  git checkout "$trt_llm_version"
   git submodule update --init --recursive
 
   # build trtllm engine
   cd /tensorrtllm_backend
-  cd ./tensorrt_llm/examples/${model_type}
+  cd "./tensorrt_llm/examples/${model_type}"
   python3 convert_checkpoint.py \
-    --model_dir ${model_path} \
-    --dtype ${model_dtype} \
-    --tp_size ${model_tp_size} \
-    --output_dir ${trt_model_path}
+    --model_dir "${model_path}" \
+    --dtype "${model_dtype}" \
+    --tp_size "${model_tp_size}" \
+    --output_dir "${trt_model_path}"
   trtllm-build \
-    --checkpoint_dir ${trt_model_path} \
+    --checkpoint_dir "${trt_model_path}" \
     --use_fused_mlp \
     --reduce_fusion disable \
     --workers 8 \
-    --gpt_attention_plugin ${model_dtype} \
-    --gemm_plugin ${model_dtype} \
-    --tp_size ${model_tp_size} \
-    --max_batch_size ${max_batch_size} \
-    --max_input_len ${max_input_len} \
-    --max_seq_len ${max_seq_len} \
-    --max_num_tokens ${max_num_tokens} \
-    --output_dir ${trt_engine_path}
+    --gpt_attention_plugin "${model_dtype}" \
+    --gemm_plugin "${model_dtype}" \
+    --tp_size "${model_tp_size}" \
+    --max_batch_size "${max_batch_size}" \
+    --max_input_len "${max_input_len}" \
+    --max_seq_len "${max_seq_len}" \
+    --max_num_tokens "${max_num_tokens}" \
+    --output_dir "${trt_engine_path}"
 
   # handle triton protobuf files and launch triton server
   cd /tensorrtllm_backend
   mkdir triton_model_repo
   cp -r all_models/inflight_batcher_llm/* triton_model_repo/
   cd triton_model_repo
   rm -rf ./tensorrt_llm/1/*
-  cp -r ${trt_engine_path}/* ./tensorrt_llm/1
+  cp -r "${trt_engine_path}"/* ./tensorrt_llm/1
   python3 ../tools/fill_template.py -i tensorrt_llm/config.pbtxt triton_backend:tensorrtllm,engine_dir:/tensorrtllm_backend/triton_model_repo/tensorrt_llm/1,decoupled_mode:true,batching_strategy:inflight_fused_batching,batch_scheduler_policy:guaranteed_no_evict,exclude_input_in_output:true,triton_max_batch_size:2048,max_queue_delay_microseconds:0,max_beam_width:1,max_queue_size:2048,enable_kv_cache_reuse:false
-  python3 ../tools/fill_template.py -i preprocessing/config.pbtxt triton_max_batch_size:2048,tokenizer_dir:$model_path,preprocessing_instance_count:5
-  python3 ../tools/fill_template.py -i postprocessing/config.pbtxt triton_max_batch_size:2048,tokenizer_dir:$model_path,postprocessing_instance_count:5,skip_special_tokens:false
-  python3 ../tools/fill_template.py -i ensemble/config.pbtxt triton_max_batch_size:$max_batch_size
-  python3 ../tools/fill_template.py -i tensorrt_llm_bls/config.pbtxt triton_max_batch_size:$max_batch_size,decoupled_mode:true,accumulate_tokens:"False",bls_instance_count:1
+  python3 ../tools/fill_template.py -i preprocessing/config.pbtxt "triton_max_batch_size:2048,tokenizer_dir:$model_path,preprocessing_instance_count:5"
+  python3 ../tools/fill_template.py -i postprocessing/config.pbtxt "triton_max_batch_size:2048,tokenizer_dir:$model_path,postprocessing_instance_count:5,skip_special_tokens:false"
+  python3 ../tools/fill_template.py -i ensemble/config.pbtxt triton_max_batch_size:"$max_batch_size"
+  python3 ../tools/fill_template.py -i tensorrt_llm_bls/config.pbtxt "triton_max_batch_size:$max_batch_size,decoupled_mode:true,accumulate_tokens:False,bls_instance_count:1"
   cd /tensorrtllm_backend
   python3 scripts/launch_triton_server.py \
-    --world_size=${model_tp_size} \
+    --world_size="${model_tp_size}" \
     --model_repo=/tensorrtllm_backend/triton_model_repo &
 
 }
 
 launch_tgi_server() {
   model=$(echo "$common_params" | jq -r '.model')
   tp=$(echo "$common_params" | jq -r '.tp')
-  dataset_name=$(echo "$common_params" | jq -r '.dataset_name')
-  dataset_path=$(echo "$common_params" | jq -r '.dataset_path')
   port=$(echo "$common_params" | jq -r '.port')
-  num_prompts=$(echo "$common_params" | jq -r '.num_prompts')
   server_args=$(json2args "$server_params")
 
   if echo "$common_params" | jq -e 'has("fp8")' >/dev/null; then
@@ -129,10 +125,7 @@ launch_tgi_server() {
 launch_lmdeploy_server() {
   model=$(echo "$common_params" | jq -r '.model')
   tp=$(echo "$common_params" | jq -r '.tp')
-  dataset_name=$(echo "$common_params" | jq -r '.dataset_name')
-  dataset_path=$(echo "$common_params" | jq -r '.dataset_path')
   port=$(echo "$common_params" | jq -r '.port')
-  num_prompts=$(echo "$common_params" | jq -r '.num_prompts')
   server_args=$(json2args "$server_params")
 
   server_command="lmdeploy serve api_server $model \
@@ -149,10 +142,7 @@ launch_sglang_server() {
 
   model=$(echo "$common_params" | jq -r '.model')
   tp=$(echo "$common_params" | jq -r '.tp')
-  dataset_name=$(echo "$common_params" | jq -r '.dataset_name')
-  dataset_path=$(echo "$common_params" | jq -r '.dataset_path')
   port=$(echo "$common_params" | jq -r '.port')
-  num_prompts=$(echo "$common_params" | jq -r '.num_prompts')
   server_args=$(json2args "$server_params")
 
   if echo "$common_params" | jq -e 'has("fp8")' >/dev/null; then
@@ -185,10 +175,7 @@ launch_vllm_server() {
 
   model=$(echo "$common_params" | jq -r '.model')
   tp=$(echo "$common_params" | jq -r '.tp')
-  dataset_name=$(echo "$common_params" | jq -r '.dataset_name')
-  dataset_path=$(echo "$common_params" | jq -r '.dataset_path')
   port=$(echo "$common_params" | jq -r '.port')
-  num_prompts=$(echo "$common_params" | jq -r '.num_prompts')
   server_args=$(json2args "$server_params")
 
   if echo "$common_params" | jq -e 'has("fp8")' >/dev/null; then
@@ -217,19 +204,19 @@ launch_vllm_server() {
 
 main() {
 
-  if [[ $CURRENT_LLM_SERVING_ENGINE == "trt" ]]; then
+  if [[ "$CURRENT_LLM_SERVING_ENGINE" == "trt" ]]; then
     launch_trt_server
   fi
 
-  if [[ $CURRENT_LLM_SERVING_ENGINE == "tgi" ]]; then
+  if [[ "$CURRENT_LLM_SERVING_ENGINE" == "tgi" ]]; then
     launch_tgi_server
   fi
 
-  if [[ $CURRENT_LLM_SERVING_ENGINE == "lmdeploy" ]]; then
+  if [[ "$CURRENT_LLM_SERVING_ENGINE" == "lmdeploy" ]]; then
     launch_lmdeploy_server
   fi
 
-  if [[ $CURRENT_LLM_SERVING_ENGINE == "sglang" ]]; then
+  if [[ "$CURRENT_LLM_SERVING_ENGINE" == "sglang" ]]; then
     launch_sglang_server
   fi
 

diff --git a/.buildkite/nightly-benchmarks/scripts/nightly-annotate.sh b/.buildkite/nightly-benchmarks/scripts/nightly-annotate.sh
@@ -16,10 +16,10 @@ main() {
     fi
 
     # initial annotation
-    description="$VLLM_SOURCE_CODE_LOC/.buildkite/nightly-benchmarks/nightly-descriptions.md"
+    #description="$VLLM_SOURCE_CODE_LOC/.buildkite/nightly-benchmarks/nightly-descriptions.md"
 
     # download results
-    cd $VLLM_SOURCE_CODE_LOC/benchmarks
+    cd "$VLLM_SOURCE_CODE_LOC/benchmarks"
     mkdir -p results/
     /workspace/buildkite-agent artifact download 'results/*nightly_results.json' results/
     ls
@@ -30,15 +30,15 @@ main() {
     /workspace/buildkite-agent artifact upload "results.zip"
 
     # upload benchmarking scripts
-    cd $VLLM_SOURCE_CODE_LOC/
+    cd "$VLLM_SOURCE_CODE_LOC/"
     zip -r nightly-benchmarks.zip .buildkite/ benchmarks/
     /workspace/buildkite-agent artifact upload "nightly-benchmarks.zip"
 
-    cd $VLLM_SOURCE_CODE_LOC/.buildkite/nightly-benchmarks/
+    cd "$VLLM_SOURCE_CODE_LOC/.buildkite/nightly-benchmarks/"
     # upload benchmarking pipeline
     /workspace/buildkite-agent artifact upload "nightly-pipeline.yaml"
 
-    cd $VLLM_SOURCE_CODE_LOC/.buildkite/nightly-benchmarks/
+    cd "$VLLM_SOURCE_CODE_LOC/.buildkite/nightly-benchmarks/"
     /workspace/buildkite-agent annotate --style "success" --context "nightly-benchmarks-results" --append < nightly-annotation.md
 
 
@@ -75,4 +75,4 @@ main() {
     # /workspace/buildkite-agent annotate --style "success" --context "nightly-benchmarks-results" --append < nightly_results.md
 }
 
-main "$@"
+main "$@"
diff --git a/.buildkite/nightly-benchmarks/scripts/run-nightly-benchmarks.sh b/.buildkite/nightly-benchmarks/scripts/run-nightly-benchmarks.sh
@@ -12,7 +12,7 @@ check_gpus() {
     echo "Need at least 1 GPU to run benchmarking."
     exit 1
   fi
-  declare -g gpu_type=$(echo $(nvidia-smi --query-gpu=name --format=csv,noheader) | awk '{print $2}')
+  declare -g gpu_type="$(nvidia-smi --query-gpu=name --format=csv,noheader | awk '{print $2}')"
   echo "GPU type is $gpu_type"
 }
 
@@ -102,7 +102,7 @@ kill_gpu_processes() {
   pkill -f text-generation
   pkill -f lmdeploy
 
-  while [ $(nvidia-smi --query-gpu=memory.used --format=csv,noheader,nounits | head -n 1) -ge 1000 ]; do
+  while [ "$(nvidia-smi --query-gpu=memory.used --format=csv,noheader,nounits | head -n 1)" -ge 1000 ]; do
     sleep 1
   done
 }
@@ -119,8 +119,8 @@ wait_for_server() {
 ensure_installed() {
   # Ensure that the given command is installed by apt-get
   local cmd=$1
-  if ! which $cmd >/dev/null; then
-    apt-get update && apt-get install -y $cmd
+  if ! which "$cmd" >/dev/null; then
+    apt-get update && apt-get install -y "$cmd"
   fi
 }
 
@@ -173,13 +173,11 @@ run_serving_tests() {
       echo "Reuse previous server for test case $test_name"
     else
       kill_gpu_processes
-      bash $VLLM_SOURCE_CODE_LOC/.buildkite/nightly-benchmarks/scripts/launch-server.sh \
+      bash "$VLLM_SOURCE_CODE_LOC/.buildkite/nightly-benchmarks/scripts/launch-server.sh" \
         "$server_params" "$common_params"
     fi
 
-    wait_for_server
-
-    if [ $? -eq 0 ]; then
+    if wait_for_server; then
       echo ""
       echo "$CURRENT_LLM_SERVING_ENGINE server is up and running."
     else
@@ -190,13 +188,13 @@ run_serving_tests() {
 
     # prepare tokenizer
     # this is required for lmdeploy.
-    cd $VLLM_SOURCE_CODE_LOC/benchmarks
+    cd "$VLLM_SOURCE_CODE_LOC/benchmarks"
     rm -rf /tokenizer_cache
     mkdir /tokenizer_cache
     python3 ../.buildkite/nightly-benchmarks/scripts/download-tokenizer.py \
       --model "$model" \
       --cachedir /tokenizer_cache
-    cd $VLLM_SOURCE_CODE_LOC/benchmarks
+    cd "$VLLM_SOURCE_CODE_LOC/benchmarks"
 
 
     # change model name for lmdeploy (it will not follow standard hf name)
@@ -307,11 +305,11 @@ run_serving_tests() {
 prepare_dataset() {
 
   # download sharegpt dataset
-  cd $VLLM_SOURCE_CODE_LOC/benchmarks
+  cd "$VLLM_SOURCE_CODE_LOC/benchmarks"
   wget https://huggingface.co/datasets/anon8231489123/ShareGPT_Vicuna_unfiltered/resolve/main/ShareGPT_V3_unfiltered_cleaned_split.json
 
   # duplicate sonnet by 4x, to allow benchmarking with input length 2048
-  cd $VLLM_SOURCE_CODE_LOC/benchmarks
+  cd "$VLLM_SOURCE_CODE_LOC/benchmarks"
   echo "" > sonnet_4x.txt
   for _ in {1..4}
   do
@@ -339,17 +337,17 @@ main() {
 
   prepare_dataset
 
-  cd $VLLM_SOURCE_CODE_LOC/benchmarks
+  cd "$VLLM_SOURCE_CODE_LOC/benchmarks"
   declare -g RESULTS_FOLDER=results/
   mkdir -p $RESULTS_FOLDER
-  BENCHMARK_ROOT=$VLLM_SOURCE_CODE_LOC/.buildkite/nightly-benchmarks/
+  BENCHMARK_ROOT="$VLLM_SOURCE_CODE_LOC/.buildkite/nightly-benchmarks/"
 
   # run the test
-  run_serving_tests $BENCHMARK_ROOT/tests/nightly-tests.json
+  run_serving_tests "$BENCHMARK_ROOT/tests/nightly-tests.json"
 
   # upload benchmark results to buildkite
   python3 -m pip install tabulate pandas
-  python3 $BENCHMARK_ROOT/scripts/summary-nightly-results.py
+  python3 "$BENCHMARK_ROOT/scripts/summary-nightly-results.py"
   upload_to_buildkite
 
 }